The use of unmanned aerial vehicles (UAVs) is attracting considerable interest in academic research, commercial, and military applications. Multi-UAV systems organized in an ad hoc fashion called a flying ad hoc network (FANET) can cooperatively and collaboratively accomplish complex missions more efficiently compared to single large UAV systems. However, the unique features of FANETs such as high mobility, low node density, and high frequency of topology changes introduce challenges to the communication design, especially routing. Thus, the routing requirements of FANETs surpass those of MANETs or VANETs. In this paper, we present UAV classification, communication and application architectures, and an exhaustive survey of the existing routing protocols for flying ad hoc networks. Furthermore, we highlight the key features, strengths and weaknesses, and different mobility models used for the performance evaluation of the existing FANET routing protocols. More importantly, a proposed taxonomy and a review on the existing FANET routing protocols are presented. Finally, we highlight the existing challenges and open research issues.

Current access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.

Aerial access networks, comprising a hierarchical model of high-altitude platforms (HAPs) and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to enhance the service experience of Internet of Things Devices (IoTD), especially in underserved areas where terrestrial base stations (TBSs) do not exist. In such scenarios, optimally orchestrating the limited computation, communication, and energy resources in both HAPs and UAVs is crucial toward for an efficient aerial networking infrastructure. Thus, in this study, we investigate and formulate the joint IoTDs association, partial offloading, and communication resource allocations (JAPORAs) decisions problem in heterogeneous Aerial Access IoT (AAIoT) networks to maximize service satisfaction for IoTDs, while minimizing their total energy consumption. In particular, the formulated problem is transformed into a multiagent Markov decision process (MAMDP) to deal with its nonconvexity and environmental dynamicity. To solve the problem, we propose a multiagent policy-gradient-based deep actor–critic algorithm, named MADDPG-JAPORA, with centralized training and decentralized execution. Our extensive numerical experiments demonstrated that MADDPG-JAPORA reliably converges and provides superior performance compared with other state-of-the-art schemes.

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques -together with SDN -are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep Q-Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime — thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.